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Data-Science
Showing all 5 resultsSorted by popularity
- Software Projects
Spam Filtering Using Supervised Machine Learning Algorithms
The objective of this project is to detect Spam and ham messages using various supervised machine learning algorithms like Naive Bayes, Support Vector Machines algorithm, Bidirectional LSTM, and Transfer Learning with USE Encoder and compare their performance in filtering the Ham and Spam messages. As people indulge more in Web-based activities, and with rising sharing of private-data by companies, SMS spam is very common. Scammers create fraudulent text messages to deceive you into giving them your personal information, such as your password, account number, or Social Security number. If they have such information, they may be able to gain access to your email, bank, or other accounts. SMS spam filter inherits much functionality from E-mail Spam Filtering. Comparative study is performed based on the performance of various supervised learning algorithms and the algorithm that gives us the most accurate result is recommended. A simple UI is developed to demonstrate the working of spam filtering in practice.
SKU: Spam Filtering Using Supervised Machine Learning Algorithms - Software Projects
Predictive Maintenance using Unsupervised Machine Learning
For any industrial machinery equipment, there is a need to increase operational flexibility and reduce operating costs. To achieve this objective, system engineers mainly focuses on 3 attributes of the machinery, namely reliability, maintainability and reliability. Maintenance strategy significantly improves the reliability and availability of assets and, as a result, decreases the number of unpredicted breakdowns. Recently unsupervised Machine Learning has received much attention in anomaly detection and predictive maintenance of equipments before they can fail. Unsupervised learning can help automate and improve feature engineering by extracting relevant and informative features from the data, without requiring labels or prior knowledge. In this project there are three different techniques that are applied: 1) PCA Model, 2) Auto-encoder Model and 3) LSTM Model with auto-encoder for detection of motor and compressor failures.
SKU: Predictive Maintenance using Unsupervised Machine Learning - Software Projects
Bank Statement Analysis and Transaction Category Prediction
Bank statement analysis involves summarizing cash inflows and outflows from statements and providing an overview of financial health of individuals. Businesses and NBFCs consider the financial history of borrowers during credit assessments and bank statement analysis tool is being used by various industries for faster processing times, efficiency, and document processing purposes. The objective of this project is to study the cashflows in terms of debits and credits for the retail customers and predict the transaction categories based on mode of transactions and counter parties etc. Various Machine Learning algorithms are used to classify the transaction categories using train data and predict the transaction categories using test data. It also recommends the algorithm that gives the best accuracy score.
SKU: Bank Statement Analysis and Transaction Category Prediction - Software Projects
Forecasting Corona Virus Outbreak
The outbreak of COVID-19 made a significant health impact across the globe. This project is designed to analyze how coronavirus affect different nations and to predict potential COVID-19 cases across all the globe on an everyday basis. The objective was to gauge COVID-19 on three metrics- confirmed cases, recovered cases and death events for the next day using historical data as on a given date. Various forecasting models such as Linear Regressor, Random Forest Regressor, ARIMA, Prophet, Holt Winter etc. are used for time series analysis and forecasting COVID cases.
SKU: Forecasting Corona Virus Outbreak - Software Projects
Malware Prediction in Software
Malwares are software viruses. Once a computer can be infected by malware, criminals can hurt consumers and enterprises in many ways. The purpose of this project is to explore and analyse the data to find out the varieties of software issues. All potential features are extracted and feature selection technique is applied to define the target variable and input feature matrix. Various classification algorithms are used to classify the potential malwares and best classifier is recommended. Model explainability is used explain the top few candidate features based on feature importance.
SKU: n/a
